Frequency of each category of crime

The data set gives information about 6878 observations on 12 different variables: the category of the crime, the persistent id (i.e. the unique identifier of that crime), the date of the crime, latitude, longitude, street ID, street name, context (extra information about the crime), ID of the crime (for the API), location type (i.e. whether it took place at a normal police force location or a British Transport location), location sub-type, and the outcome status. To get a better picture of the crime data, a bar chart was plotted showing the different crime types and their relative frequencies.

library(dplyr)
library(ggplot2)

freq_of_each_category <- crime_data %>%
  count(category) 

freq_of_each_category %>%
  ggplot(aes(x = reorder(category, n), 
             y = n)) + 
  geom_col(fill = "cadetblue3") + labs(x = "Category of Crime", 
                    y = "Frequency of Crime",
                    title = "Violent crime was the most common category") + coord_flip()

The bar chart shows that, by far, the most common type of crime was violent crime, with approximately 2600 incidents that year - a large contrast in comparison to the next most common type of crime, anti-social behavior, which had approximately 700 incidents reported that year. This could be due to the fact that violent crime encompasses a range of offenses, from common assault and Actual Bodily Harm, all the way to murder (1), and so it would be interesting to see the frequencies for each category which falls within violent crime to get a better idea of what was the most common type of crime. The categories with the lowest frequency of incidents were robbery, other crime, theft from the person, and possession of weapons, each with less than 100 incidents that year.

Proportion of outcomes

Next, a series of pie charts were plotted to view the proportion of the outcomes within each crime category:

#create pie charts

ggplot(crime_data, aes(x="",  fill = factor(outcome_status))) +
  geom_bar(stat = "count", width = 1, position =  "fill") + 
  coord_polar("y", start=0) + 
  theme_void() +
  facet_wrap(~ category, nrow = 4, labeller = labeller(category = label_wrap_gen(width = 15))) +
  scale_fill_manual(values = c("lightgoldenrod","orchid3","orange", "lightpink","lightsteelblue", "rosybrown", "cadetblue3", "royalblue4","seagreen", "plum", "hotpink","lightskyblue4", "darkolivegreen3", "slategray3")) +
  theme(legend.position = "bottom",
        legend.text.align = 0,
        strip.placement = "outside",
        strip.margin = margin(b=20)) +
  labs(fill = "Outcome Status") +
  guides(fill = guide_legend(nrow = 7))

The pie charts show that the outcome “investigation complete; no suspect identified” had the highest proportion among all the outcomes, and that the categories bicycle theft, burglary, arson, other theft, theft from the person, and vehicle crime in particular were most likely to have this outcome. This could be due to the fact that these categories are all most likely to be reported after the incident has taken place, meaning it is a lot harder to find the perpetrator, whereas almost all the other crimes (i.e. anti-social behavior, drugs, other crime, possession of weapons) are crimes in which the police are most likely to catch them in the act. For bicycle theft, theft from the person and vehicle crime in particular, where the damage is relatively minimal, lack of time and police resources could also contribute to being unable to identify the suspect as it would be too costly to find the perpetrators for such small crimes.

Unfortunately, despite having the highest frequency of the crimes, the pie charts also show that violent crime has the highest proportion of the category ‘unable to prosecute suspect’, with around 50% of all violent crime resulting in this outcome. This could be due to the fact that a lot of offences that fall under this category (e.g. common assault, Actual Bodily Harm, Grievous Bodily Harm) rely on hearsay if the incident had occurred in a secluded area for example, and therefore may have insufficient evidence to prosecute the suspect. Again, more information on the categories within violent crime committed (rather than violent crime as a general category) would be needed in order to analyse why there is such a high proportion of of suspects being unable to be prosecuted.

Map of category of crimes

library(leaflet)

crime_data %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(data = crime_data[crime_data$category=="anti-social-behaviour",], radius = 3, group = "Anti-social Behaviour") %>% 
  addCircleMarkers(data = crime_data[crime_data$category=="bicycle-theft",], radius = 3, group = "Bicycle Theft") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="burglary",], radius = 3, group = "Burglary") %>%
   addCircleMarkers(data = crime_data[crime_data$category=="criminal-damage-arson",], radius = 3, group = "Criminal Damage Arson") %>%
 addCircleMarkers(data = crime_data[crime_data$category=="drugs",], radius = 3, group = "Drugs") %>%
   addCircleMarkers(data = crime_data[crime_data$category=="other-theft",], radius = 3, group = "Other Theft") %>%
   addCircleMarkers(data = crime_data[crime_data$category=="possession-of-weapons",], radius = 3, group = "Possession of Weapons") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="public-order",], radius = 3, group = "Public Order") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="robbery",], radius = 3, group = "Robbery") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="shoplifting",], radius = 3, group = "Shoplifting") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="theft-from-the-person",], radius = 3, group = "Theft From The Person") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="vehicle-crime",], radius = 3, group = "Vehicle Crime") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="violent-crime",], radius = 3, group = "Violent Crime") %>%
  addCircleMarkers(data = crime_data[crime_data$category=="other-crime",], radius = 3, group = "Other Crime") %>%
  addLayersControl(
    overlayGroups = c("Anti-social Behaviour", "Bicycle Theft", "Burglary", "Criminal Damage Arson", "Drugs", "Other Theft", "Possession of Weapons", "Public Order", "Robbery", "Shoplifting", "Theft From The Person", "Vehicle Crime", "Violent Crime", "Other Crime"),  # Creates layer control for each category
    options = layersControlOptions(collapsed = FALSE)
  )

+−

Anti-social Behaviour

Bicycle Theft

Burglary

Criminal Damage Arson

Drugs

Other Theft

Possession of Weapons

Public Order

Robbery

Shoplifting

Theft From The Person

Vehicle Crime

Violent Crime

Other Crime

Leaflet | © OpenStreetMap, ODbL

From the map above, if we view each of the categories one at a time, we can see that most crimes tend to happen in the city centre, aside from burglary, vehicle crime, violent crime, and other crime, which all tend to be slightly more spread out. This is understandable since there are a higher density of people in the city centre and so there would not only be more people to commit and witness crimes, but there would also be more opportunities to commit crimes as well, for example with anti-social behaviour, bicycle theft, public order, shoplifting, and theft from the person. On the other hand, although this also applies to burglary, vehicle crime, and violent crime, these crimes might also be more spread out because these crimes can happen in secluded areas as well.

Frequency of location types

#calculate frequencies for each location type
Force_freq <- sum(crime_data$location_type == "Force")
BTP_freq <- sum(crime_data$location_type == "BTP")

#create data frame
Location_type_df <- data.frame(
  Location_Type = c("Force", "BTP"), 
  Frequency = c(Force_freq, BTP_freq))

#create table
knitr::kable(Location_type_df,align = "cccc",caption="Table showing the frequencies of each location type.")

The frequency table shows the number of crimes that took place on a normal police force location (Force) and the number that took place on a British Transport location, which is the policing area for the UK rail network. As one would expect, there is a much higher number that took place on a normal police force location, as generally this tends to cover a much larger area. A more detailed analysis on the proportions of the different crime categories and their type of location is covered below.

Proportion of crime categories according to location type

ggplot(crime_data, aes(x="",  fill = category)) +
  geom_bar(stat = "count", width = 1, position =  "fill") + 
  coord_polar("y", start=0) + 
  theme_void() +
  facet_wrap(~ location_type, nrow = 1) +
  scale_fill_manual(values = c("orange","orchid3", "lightgoldenrod", "darkolivegreen3","lightsteelblue4", "plum","lightpink","ivory2", "rosybrown3", "hotpink","lightskyblue4", "seagreen", "royalblue4", "cadetblue3"))

As violent crime had by far the highest frequency and was relatively spread out throughout the map, it is unsurprising that this category had the highest proportion in both the BTP locations and Force locations, and that categories such as shoplifting and burglary are not present in the BTP locations. What is interesting to note, however, is that the BTP locations had a much higher proportion of crimes that fall under the bicycle theft category, as one would think that bicycle theft would happen more in housing areas or near shops, rather than the railway network. This could be due to a higher density of bicycles being parked in these areas making it easier for perpetrators to find bicycles that are not secured so well.

The pie charts also show that the BTP locations also had a higher proportion of the ‘other theft’ category compared to the Force locations. This could be attributed to people accidentally leaving their possessions either on the train or at the train station, which is a lot easier for perpetrators to steal.

Additionally, they also show that there were a higher proportion of crimes that fall under the ‘public order’ category in the BTP locations. For clarification, public order includes threatening unlawful violence, using abusive or insulting words or behaviour, stirring up racial hatred, and offences in connection with alcohol and trains, among other things (2).

However, it is also important to note that as there are only 24 BTP incidents in total in comparison to the 6854 for Force locations, this could result in certain types of crime being under or over-represented for the BTP, and so more data (perhaps from various years) would be needed to see if these proportions are a general trend for the BTP locations.

Amount of crime over the course of 2023

library("tidyverse")
library("lubridate")

crime_data$date <- as.Date(paste0(crime_data$date, "-01")) #convert dates into date format

date_occurances <- crime_data %>% count(date)
  
date_occurances %>%
  ggplot(aes(x = date, y = n)) +
  geom_line() +
  scale_x_date(date_labels = "%b-%Y", date_breaks = "2 month") +
  labs(title = "Crime Frequency Over Course of Year",
       x = "Month",
       y = "Frequency") +
  theme_minimal()

The above graph shows the number of crimes that took place over the course of 2023. Interestingly, according to the graph, the highest frequency of crime happens in January with roughly 650 incidents, then there is a steep drop in the number in February to around 470, which is the lowest frequency during the year. Then, after some fluctuation during March to August between the 550 to 585 marks, the frequency rises again to circa 640 in September, before steadily declining until December, which had roughly 550 incidents. One possible explanation for the high amount of crime in January could be that it is the time period right after Christmas, and so people may be generally poorer from buying numerous gifts and therefore more inclined to shoplift. Additionally, people having more expensive things on display, either on the person or at their home, may also inadvertently make them be more susceptible to robbery, thievery, or burglary. This could also explain the steep decline of crimes in February, as perhaps these perpetrators may not feel the need to commit these crimes so soon afterwards.

The following section will analyse whether various aspects of the weather could also have had a role to play on the number and type of crimes committed throughout the year.

Temperature

library("plotly")

weather_data$Date <- as.Date(weather_data$Date, format = "%d/%m/%Y")

weather_data <- weather_data %>% 
  mutate(Month = format(Date, "%b"), 
         Month = factor(Month, levels = month.abb))

Temperature2023 <- ggplot(weather_data, aes(x = Month, y = TemperatureCAvg, fill = Month)) +
  geom_boxplot() + 
  guides(fill = "none") + 
  labs(title = "Box Plots Showing Monthly Average Temperature", 
       x = "Month", 
       y = "Temperature (°C)") + 
  scale_fill_brewer(palette = "Set3")
ggplotly(Temperature2023, height = 500, width = 1000)